Establishment of a clinical diagnostic model for gouty arthritis based on the serum biochemical profile: A case-control study. Issue 16 (23rd April 2021)

• Record Type: Journal Article
• Authors: Lyu, Shang; Ding, Ruowen; Yang, Shilin; Chen, Wanyuan; Rao, Yi; OuYang, Hui; Liu, Peng; Feng, Yulin
• Other Names: Gouri. Adel section editor.
• Is Part Of: Medicine. Volume 100:Issue 16(2021)
• Publication Date: 2021-04-23
• Subjects: clinical diagnostic model -- disease progression -- gouty arthritis -- multiple logistic regression analysis -- serum biochemical profile; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000025542
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : Abstract: The disease progression of gouty arthritis (GA) is relatively clear, with the 4 stages of hyperuricemia (HUA), acute gouty arthritis (AGA), gouty arthritis during the intermittent period (GIP), and chronic gouty arthritis (CGA). This paper attempts to construct a clinical diagnostic model based on blood routine test data, in order to avoid the need for bursa fluid examination and other tedious steps, and at the same time to predict the development direction of GA. Serum samples from 579 subjects were collected within 3 years in this study and were divided into a training set (n = 379) and validation set (n = 200). After a series of multivariate statistical analyses, the serum biochemical profile was obtained, which could effectively distinguish different stages of GA. A clinical diagnosis model based on the biochemical index of the training set was established to maximize the probability of the stage as a diagnosis, and the serum biochemical data from 200 patients were used for validation. The total area under the curve (AUC) of the clinical diagnostic model was 0.9534, and the AUCs of the 5 models were 0.9814 (Control), 0.9288 (HUA), 0.9752 (AGA), 0.9056 (GIP), and 0.9759 (CGA). The kappa coefficient of the clinical diagnostic model was 0.80. This clinical diagnostic model could be applied clinically and in research to improve the accuracy of the identification of the different stages of GA. Meanwhile, the serum biochemical profile revealed by this study could be used to assist the clinical diagnosis and prediction of GA. Abstract : Supplemental Digital Content is available in the text